One approach to dealing with this problem may be create models that account for differences between datasets. More generally, this approach is known as domain adaptation. One specific way to implement this, initially proposed by Alvi et al [1]}, is so-called `Joint Learning and Unlearning'. This uses an adversarial multi-task approach to simultaneously minimise domain (i.e. data set) prediction accuracy and maximise task accuracy. This approach has been successfully used for MRI segmentation problems, and we have begun investigating how it may be used for ECG data [2]}.
